Archive for the ‘mood’ Category


Using Visualizations for Music Discovery

October 22, 2009

Hot of the presses, here are the sides for the tutorial that Justin and Paul are presenting at ISMIR 2009 on October 26.

Note that the live presentation will include many demonstrations and videos of visualizations that just are not practical to include in a PDF.  If you have the chance, be sure to check out the tutorial at ISMIR in Kobe on the 26th.


Visualizing emotion in lyrics

September 11, 2009


Joris Klerkx has built a visualizer of the emotions in lyrics.  Joris has  integrated a karaoke player and Synesketch, a framework for visualizing 6 basic emotions, defined by Ekman (happiness, anger, fear, surprise, sadness, disgust). The player takes a song, plays it, and with each line of text that plays in the lyrics, the strongest emotion of that line is visualized.  In the image above, on the left hand side, you’ll see the 6 emotions and their visualization. On the right hand side, 2 screenshots of demo’s of the prototype.
Some video of the player in action:

  • Thriller by Michael Jackson: emotions fear, angry, sad & disgust are well visible in the end.
  • Shiny Happy People by REM: pretty happy.

Jorik points out that it can be interesting to see how the visualizations contrast with how the song sounds since offten times the emotion and mood of the lyrics of a song contrast with how the song sounds



Mr. Emo: Music Retrieval in the emotion plane

September 7, 2009


This technical demo presents a novel emotion-based music retrieval platform, called Mr. Emo, for organizing and browsing music collections. Unlike conventional approaches which quantize emotions into classes, Mr. Emo defines emotions by two continuous variables arousal and valence and employs regression algorithms to predict them. Associated with arousal and valence values (AV values), each music sample becomes a point in the arousal-valence emotion plane, so a user can easily retrieve music samples of certain emotion(s) by specifying a point or a trajectory in the emotion plane. Being content centric and functionally powerful, such emotion-based retrieval complements traditional keyword- or artist-based retrieval. The demo shows the
effectiveness and novelty of music retrieval in the emotion plane.


Yi-Hsuan Yang, Yu-Ching Lin, Heng-Tze Cheng, and Homer Chen
National Taiwan University

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